OK, now generate a basic convolution test on two small arrays. I will make it easy by using a batch size of 1, and since time series are 1-dimensional, I will have an "image height" of 1. And since it's a univariate time series, clearly the number of "channels" is also 1, so this will be simple, right?

is about applying the filter along the wrong axis, I guess, although there are two forms.

But then the axes along which I can apply the filter are confusing too - notice that it actually constructs the graph with input shape (5, 1, 1, 1) and filter shape (1, 1, 1, 3). AFAICT from the documentation, this should be a filter that looks at on example from the batch, one "pixel" and one "channel" and outputs 3 "channels". Why does that one work, then, when others do not?

Anyway, sometimes it does not fail while constructing the graph.
Sometime it constructs the graph; then we get the

tensorflow.python.framework.errors.InvalidArgumentError

. From some confusing github tickets I gather this is probably due to the fact that I'm running on CPU instead of GPU, or vice versa the fact that the convolution Op is only defined for 32 bit floats, not 64 bit floats. If anyone could throw some light on which axes I should be aligning what on, in order to convolve a time series with a kernel, I'd be very grateful.